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.ipynb .pdf

Contents

# Title

import pandas as pd

col_names ={'year':'UTC',
            'mo':'UTC',
            'day':'UTC',
            'hr':'UTC',
            'yday':'UTC',
            'BPR':'mbar',
            'ATMP':'degC',
            'STMP':'degC',
            'WNDE':'m/s',
            'WNDN':'m/s',
            'WSPD':'m/s',
            'WDIR':'deg',
            'RH':'%',
            'SRAD':'W/m2',
            'LRAD':'W/m2',
            'PRC':'mm',
            'PRATE':'mm/hr',
            'SAL':'l',
            'COND':'S/m'}

#def get_whots_system():

# WHOTS SYSTEM 1QC and 2QC DATA
whots17_sys1_qc=['http://uop.whoi.edu/currentprojects/WHOTS/data/WHOTS-XVII_MET_sys1QC.txt']
whots17_sys2_qc=['http://uop.whoi.edu/currentprojects/WHOTS/data/WHOTS-XVII_MET_sys2QC.txt']

# WHOTS SYSTEM 1 and 2 NOT QC DATA
whots17_sys1=['http://uop.whoi.edu/currentprojects/WHOTS/data/WHOTS-XVII_MET_sys1.txt']
whots17_sys2=['http://uop.whoi.edu/currentprojects/WHOTS/data/WHOTS-XVII_MET_sys2.txt']

whots17_sys1=['http://uop.whoi.edu/currentprojects/WHOTS/data/WHOTS-XVII_MET_sys1.txt']
whots17_sys2=['http://uop.whoi.edu/currentprojects/WHOTS/data/WHOTS-XVII_MET_sys2.txt']

for system,url in enumerate([whots17_sys1,whots17_sys2]):
    df = pd.read_csv(url[0],
                     skiprows = 5,
                     delim_whitespace=True,
                     names  = col_names.keys(),
                     index_col= False
                        )

    df.index=(df['year'].map(str) +
                 '-' + df['mo'].map(str) +
                 '-' + df['day'].map(str) +
                 ' ' + df['hr'].map(str)
                 )

    if system == 0:
        df1 = df # save system 1
    else:
        df2 = df # save system 2

from ipywidgets import Button, HBox
import plotly.offline as py
import plotly.graph_objs as go
from plotly.subplots import make_subplots

# To initialize plotly for notebook usage
py.init_notebook_mode()

plot_all_whots=(['RH', 'ATMP', 'BPR', 'SRAD', 'LRAD'],   # First Plot
                ['STMP', 'SAL', 'COND', 'PRC', 'PRATE'], # Second Plot
                ['WSPD', 'WDIR', 'WNDN', 'WNDE','BPR']) # Third Plot


# Create 3 figures with 5 subplots each
for counter,variable in enumerate(plot_all_whots):

    # Create subplots
    fig = make_subplots(
          rows=5, cols=1,
          shared_xaxes=True,
          vertical_spacing=0.05,
          specs=[[{"type": "scatter"}],
                 [{"type": "scatter"}],
                 [{"type": "scatter"}],
                 [{"type": "scatter"}],
                 [{"type": "scatter"}]],
          subplot_titles=plot_all_whots[counter][:],

    )

    # Plot system 1
    for counter1, variable1 in enumerate(plot_all_whots[counter][:]):
        # Plot variables
        fig.add_trace(
            go.Scatter(
                x=df1.index,
                y=df1[variable1],
                mode="lines",
                name= variable1,
                line=dict(color='blue', width=1.5)
            ),
            row=counter1+1, col=1
        )

    # Plot system 2
    for counter2, variable2 in enumerate(plot_all_whots[counter][:]):

        # Plot variables
        fig.add_trace(
            go.Scatter(
                x=df2.index,
                y=df2[variable2],
                mode="lines",
                name= variable2,
                line=dict(color='red', width=1)
            ),

            row=counter2+1, col=1
        )

    # Add buttons
    fig.update_xaxes(
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1d", step="day", stepmode="backward"),
                dict(count=15, label="15d", step="day", stepmode="backward"),
                dict(count=1, label="1m", step="month", stepmode="backward"),
                dict(count=1, label="1y", step="year", stepmode="backward"),
                dict(step="all")
            ])
        ),
    rangeslider= {'visible':False}, row=1, col=1,

    )

    fig['layout'].update(height=800)
    fig.update_layout(xaxis=dict(tickformat="%mm"))
    fig.show()

plot_all_whots = {
    "plot1":{
        "RH": "%",
        "ATMP": "degC",
        "BPR": "mbar",
        "SRAD": "W/m2",
        "LRAD":"W/m2"
    },
    "plot2":{
        "STMP": "degC",
        "SAL": "1",
        "COND": "S/m",
        "PRC": "mm",
        "PRATE":"mm/hr"
    },
    "plot3":{
        "WSPD": "m/s",
        "WDIR": "deg",
        "WNDN": "m/s",
        "WNDE": "m/s",
        "ATMP":"m/s"
    }
}

# Calculating the means
monthly_means = df2.groupby("mo").median()
hourly_means = df2.groupby("hr").median()
daily_means = df2.groupby("day").median()

monthly_means['STMP'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fa80219bd00>

import ipywidgets as widgets

# Create buttons for Stations and Sensors
stationW = widgets.ToggleButtons(options='Stations', orientation='horizontal')